Large-scale value-based non-linear models often require large numbers of decision variables and constraints (e.g., over a million), potentially with vastly infeasible search spaces (e.g., discovery of feasible decision variable sets without a vastly infeasible search space mechanism may be relatively difficult or impossible). Multi-objective Evolutionary Algorithms (MOEA) may fail when they have such a large number of decision variables and constraints. Often times in these MOEA, the computational bandwidth needed to evaluate whether a potential solution violates a constraint may be relatively high. In some cases, determining whether potential solutions violate constraints may require similar or even more computational bandwidth than evaluations of potential solutions on the basis of one or more objectives. In many cases, there may be oceans of unfeasible space in the objective hyper-dimensional space. These large oceans of unfeasible space can sometimes pose particular difficulty in converging to multi-objective optimized solutions.